• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.11
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• pp.1049-1059
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• 2010

This paper treats the aerodynamic optimization of the blade planform for helicopters. The blade shapes, which should be determined during the threedimensional aerodynamic configuration design step, are defined and are parameterized using the B$\acute{e}$zier curves. This research focuses on the design approaches generally adopted by industries and or research institutes using their own experiences and know-hows for the parameterization and for the definition of design constraints. The hover figure of merit and the equivalent lift-to-drag ratio for the forward flight are used to define the objective function. The resultant nonlinear programming (NLP) problem is solved using the sequential quadratic programming (SQP) method. The applications show the present method can design the important planform shapes such as the airfoil distribution, twist and chord variations in the efficient manner.

• Structural Engineering and Mechanics
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• v.40 no.1
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• pp.85-103
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• 2011

Integration of finite element analysis (FEA) software into various software platforms is commonly used in coupling systems such as systems involving structural control, fluid-structure, wind-structure, soil-structure interactions and substructure method in which FEA is used for simulating the structural responses. Integrating an FEA program into various other software platforms in an efficient and simple way is crucial for the development and performance of the entire coupling system. The lack of simplicity of the existing integration methods makes this integration difficult and therefore entails the motivation of this study. In this paper, a novel practical technique, namely CS technique, is presented for integrating a general FEA software framework OpenSees into other software platforms, e.g., Matlab-$Simulink^{(R)}$ and a soil-structure interaction (SSI) system. The advantage of this integration technique is that it is efficient and relatively easy to implement. Instead of OpenSees, a cheap client handling TCL is integrated into the other software. The integration is achieved by extending the concept of internet based client-server concept, taking advantage of the parameterization framework of OpenSees, and using a command-driven scripting language called tool command language (TCL) on which the OpenSees' interface is based. There is no need for any programming inside OpenSees. The presented CS technique proves as an excellent solution for the coupling problems mentioned above (for both linear and nonlinear problems). Application examples are provided to validate the integration method and illustrate the various uses of the method in the civil engineering.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.